Tremendous progresses have been achieved in development of land process models over the last two decades. The land models, in couple with earth system models, now simulate the land-atmosphere interactions via biophysical and biogeochemical processes. These models are now widely used assessing management and policy options for climate change mitigation and adaptation. However, model intercomparison and data-model comparison have clearly showed large uncertainties in predicted ecosystem-climate change feedbacks. To reduce uncertainties in model predictions, it is essential to continuously evaluate and improve models against experimental and observational data. The overall objective of this project is to improve land process models for predicting responses and feedback of terrestrial ecosystems to global change. We will achieve the objective via development and application of various data synthesis and data assimilation techniques at global change experiments, FLUXNET (including AmeriFlux), and other studies to identify general mechanisms and estimate parameters for model improvement. Specifically, this project will improve land models in terms of 1) temperature response functions of ecosystem processes; 2) data products to constrain modeled ecosystem feedback to climate change; 3) baseline performance of soil carbon distribution; 4) computational efficiency; 5) parameter variability under various climate change scenarios; and 6) uncertainty analysis. This project will directly support the Long Term Performance Measure (LTM) of the DOE’s global change research to “Deliver improved scientific data and models about the potential response of the Earth’s climate and terrestrial biosphere to increased greenhouse gas levels for policy makers to determine safe levels of greenhouse gases in the atmosphere”. The project is designed to directly improve models at scales from ecosystem to regions and the globe.